@inproceedings{ma-etal-2025-semantic,
    title = "Semantic Prosody in Machine Translation: the {E}nglish-{C}hinese Case of Passive Structures",
    author = "Ma, Xinyue  and
      Pastells, Pol  and
      Delor, Mariona Taul{\'e}  and
      Farr{\'u}s, Mireia",
    editor = "Frermann, Lea  and
      Stevenson, Mark",
    booktitle = "Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025)",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.starsem-1.5/",
    pages = "59--69",
    ISBN = "979-8-89176-340-1",
    abstract = "Semantic prosody is a collocational meaning formed through the co-occurrence of a linguistic unit and a consistent series of collocates, which should be treated separately from semantic meaning. Since words that are literal translation of each other may have different semantic prosody, more attention should be paid to this linguistic property in order to generate accurate translation. However, current machine translation models cannot handle this problem. To bridge the gap, we propose an approach to teach machine translation models about semantic prosody of a specific structure. We focus on Chinese BEI passives and create a dataset of English-Chinese sentence pairs with the purpose of demonstrating the negative semantic prosody of BEI passives. Then we fine-tune OPUS-MT, NLLB-600M and mBART50-mmt models with our dataset for the English-Chinese translation task. Our results show that fine-tuned MT models perform better on using BEI passives for translating unfavourable content and avoid using it for neutral and favourable content. Also, in NLLB-600M, which is a multilingual model, this knowledge of semantic prosody can be transferred from English-Chinese translation to other language pairs, such as Spanish-Chinese."
}